Education

Sep 2009Jun 2012

Master of Information Technology

Urine analysis reveals the presence of many problems and diseases in human body. Manual microscopic urine analysis is time consuming, subjective to human observation, and causes mistakes. Computer aided automatic microscopic analysis can overcome these problems.

This research introduced a comprehensive approach for automating procedures for detecting and recognition of microscopic urine particles. Samples of RBC, WBC, epithelial cell, calcium oxalate, triple phosphate, and other undefined images were used in experiments. Experiment was applied in two tracks, first considered vague particles which have light boundaries, and second considered the solid particles which have strong boundaries.

In first experiment, images were segmented, textural features were extracted, features’ selection was applied, and five classifiers were tested to get the best results where accuracy of 90.16% was got. In second experiment, image processing functions and segmentation were applied, shape and textural features were extracted, and five classifiers were tested to get the best results where accuracy of 96.41% was got. Repeated experiments were done for adjusting factors to produce the best evaluation results. A very good performance was achieved compared with many related works.

To download the set of images used by this research click the following link